# -*- coding: utf-8 -*-
"""
-------------------------------------------------
File Name： sine_position_embedding
Description :
Author : 'li'
date： 2022/6/29
Change Activity:
2022/6/29:
-------------------------------------------------
"""
import math

import torch

from ...model.base import BaseModule


class SinePositionEmbedding(BaseModule):
    def __init__(self, num_pos_feats=64, temperature=10000, normalize=False, scale=None):
        """

        Args:
            num_pos_feats: output channels = num_pos_feats*2
            temperature:
            normalize:
            scale:

        Returns:

        """
        super().__init__()
        self.num_pos_feats = num_pos_feats
        self.temperature = temperature
        self.normalize = normalize
        if scale is not None and normalize is False:
            raise ValueError("normalize should be True if scale is passed")
        if scale is None:
            scale = 2 * math.pi
        self.scale = scale

    def forward(self, x, mask=None):
        """

        Args:
            x: with shape (batch_size,channels_number,h,w)
            mask:(batch_size,h,w)

        Returns:

        """
        if mask is None:
            batch_size, channels_number, h, w = x.shape
            mask = torch.ones((batch_size, h, w))
            mask = mask.to(torch.bool)
        y_embed = mask.cumsum(1, dtype=torch.float32)
        x_embed = mask.cumsum(2, dtype=torch.float32)
        if self.normalize:
            eps = 1e-6
            y_embed = y_embed / (y_embed[:, -1:, :] + eps) * self.scale
            x_embed = x_embed / (x_embed[:, :, -1:] + eps) * self.scale

        dim_t = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device)
        dim_t = self.temperature ** (2 * (dim_t // 2) / self.num_pos_feats)

        pos_x = x_embed[:, :, :, None] / dim_t
        pos_y = y_embed[:, :, :, None] / dim_t
        pos_x = torch.stack((pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), dim=4).flatten(3)
        pos_y = torch.stack((pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), dim=4).flatten(3)
        pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2)
        return pos
